Overview

Brought to you by YData

Dataset statistics

Number of variables8
Number of observations331
Missing cells20
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.7 KiB
Average record size in memory110.4 B

Variable types

Numeric8

Alerts

prior_insutruction_reporting_frequency is highly overall correlated with prior_insutruction_reporting_frequency_scaled_by_max and 2 other fieldsHigh correlation
prior_insutruction_reporting_frequency_scaled_by_max is highly overall correlated with prior_insutruction_reporting_frequency and 2 other fieldsHigh correlation
unique_practice_questions_answered is highly overall correlated with prior_insutruction_reporting_frequency and 2 other fieldsHigh correlation
unique_practice_questions_answered_scaled_by_max is highly overall correlated with prior_insutruction_reporting_frequency and 2 other fieldsHigh correlation
proportion_of_prior_insutrction has 20 (6.0%) missing valuesMissing
user_id has unique valuesUnique
prior_insutruction_reporting_frequency has 20 (6.0%) zerosZeros
proportion_of_prior_insutrction has 118 (35.6%) zerosZeros
prior_insutruction_reporting_frequency_scaled_by_max has 20 (6.0%) zerosZeros
exam_points has 4 (1.2%) zerosZeros

Reproduction

Analysis started2024-09-15 18:30:47.033905
Analysis finished2024-09-15 18:30:56.560601
Duration9.53 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

user_id
Real number (ℝ)

UNIQUE 

Distinct331
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46800.384
Minimum257
Maximum69968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-09-15T14:30:56.592871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum257
5-th percentile11522.5
Q132484.5
median56118
Q360575
95-th percentile66229
Maximum69968
Range69711
Interquartile range (IQR)28090.5

Descriptive statistics

Standard deviation17681.555
Coefficient of variation (CV)0.3778079
Kurtosis-0.56023674
Mean46800.384
Median Absolute Deviation (MAD)8580
Skewness-0.76235969
Sum15490927
Variance3.1263737 × 108
MonotonicityNot monotonic
2024-09-15T14:30:56.650489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42930 1
 
0.3%
58618 1
 
0.3%
59561 1
 
0.3%
59019 1
 
0.3%
59566 1
 
0.3%
15297 1
 
0.3%
59435 1
 
0.3%
11486 1
 
0.3%
58148 1
 
0.3%
11559 1
 
0.3%
Other values (321) 321
97.0%
ValueCountFrequency (%)
257 1
0.3%
427 1
0.3%
586 1
0.3%
4124 1
0.3%
4653 1
0.3%
5549 1
0.3%
7604 1
0.3%
7751 1
0.3%
8167 1
0.3%
8887 1
0.3%
ValueCountFrequency (%)
69968 1
0.3%
69833 1
0.3%
69067 1
0.3%
69042 1
0.3%
68851 1
0.3%
68836 1
0.3%
68668 1
0.3%
68592 1
0.3%
68529 1
0.3%
68415 1
0.3%

prior_insutruction_reporting_frequency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.087613
Minimum0
Maximum16
Zeros20
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-09-15T14:30:56.699100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median16
Q316
95-th percentile16
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.2813162
Coefficient of variation (CV)0.40353547
Kurtosis0.77811678
Mean13.087613
Median Absolute Deviation (MAD)0
Skewness-1.5466829
Sum4332
Variance27.892301
MonotonicityDecreasing
2024-09-15T14:30:56.744233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
16 229
69.2%
0 20
 
6.0%
15 17
 
5.1%
3 12
 
3.6%
7 6
 
1.8%
8 6
 
1.8%
9 5
 
1.5%
5 5
 
1.5%
4 5
 
1.5%
1 5
 
1.5%
Other values (7) 21
 
6.3%
ValueCountFrequency (%)
0 20
6.0%
1 5
 
1.5%
2 3
 
0.9%
3 12
3.6%
4 5
 
1.5%
5 5
 
1.5%
6 4
 
1.2%
7 6
 
1.8%
8 6
 
1.8%
9 5
 
1.5%
ValueCountFrequency (%)
16 229
69.2%
15 17
 
5.1%
14 1
 
0.3%
13 2
 
0.6%
12 4
 
1.2%
11 4
 
1.2%
10 3
 
0.9%
9 5
 
1.5%
8 6
 
1.8%
7 6
 
1.8%

proportion_of_prior_insutrction
Real number (ℝ)

MISSING  ZEROS 

Distinct37
Distinct (%)11.9%
Missing20
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean0.31605606
Minimum0
Maximum1
Zeros118
Zeros (%)35.6%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-09-15T14:30:56.798669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.16666667
Q30.58571429
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.58571429

Descriptive statistics

Standard deviation0.35510523
Coefficient of variation (CV)1.1235514
Kurtosis-0.95286937
Mean0.31605606
Median Absolute Deviation (MAD)0.16666667
Skewness0.74092614
Sum98.293436
Variance0.12609972
MonotonicityNot monotonic
2024-09-15T14:30:56.855425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 118
35.6%
1 20
 
6.0%
0.0625 18
 
5.4%
0.125 14
 
4.2%
0.9375 13
 
3.9%
0.875 13
 
3.9%
0.1875 12
 
3.6%
0.5625 10
 
3.0%
0.25 9
 
2.7%
0.5 9
 
2.7%
Other values (27) 75
22.7%
(Missing) 20
 
6.0%
ValueCountFrequency (%)
0 118
35.6%
0.0625 18
 
5.4%
0.06666666667 1
 
0.3%
0.07692307692 1
 
0.3%
0.09090909091 1
 
0.3%
0.1111111111 2
 
0.6%
0.125 14
 
4.2%
0.1666666667 1
 
0.3%
0.1875 12
 
3.6%
0.2 4
 
1.2%
ValueCountFrequency (%)
1 20
6.0%
0.9375 13
3.9%
0.9333333333 2
 
0.6%
0.875 13
3.9%
0.8666666667 1
 
0.3%
0.8125 5
 
1.5%
0.75 4
 
1.2%
0.7142857143 1
 
0.3%
0.6875 7
 
2.1%
0.6666666667 3
 
0.9%

prior_insutruction_reporting_frequency_scaled_by_max
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81797583
Minimum0
Maximum1
Zeros20
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-09-15T14:30:56.904903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8125
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.1875

Descriptive statistics

Standard deviation0.33008226
Coefficient of variation (CV)0.40353547
Kurtosis0.77811678
Mean0.81797583
Median Absolute Deviation (MAD)0
Skewness-1.5466829
Sum270.75
Variance0.1089543
MonotonicityDecreasing
2024-09-15T14:30:56.951281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 229
69.2%
0 20
 
6.0%
0.9375 17
 
5.1%
0.1875 12
 
3.6%
0.4375 6
 
1.8%
0.5 6
 
1.8%
0.5625 5
 
1.5%
0.3125 5
 
1.5%
0.25 5
 
1.5%
0.0625 5
 
1.5%
Other values (7) 21
 
6.3%
ValueCountFrequency (%)
0 20
6.0%
0.0625 5
 
1.5%
0.125 3
 
0.9%
0.1875 12
3.6%
0.25 5
 
1.5%
0.3125 5
 
1.5%
0.375 4
 
1.2%
0.4375 6
 
1.8%
0.5 6
 
1.8%
0.5625 5
 
1.5%
ValueCountFrequency (%)
1 229
69.2%
0.9375 17
 
5.1%
0.875 1
 
0.3%
0.8125 2
 
0.6%
0.75 4
 
1.2%
0.6875 4
 
1.2%
0.625 3
 
0.9%
0.5625 5
 
1.5%
0.5 6
 
1.8%
0.4375 6
 
1.8%

exam_points
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.770393
Minimum0
Maximum15
Zeros4
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-09-15T14:30:56.993484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median11
Q312
95-th percentile14
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.6937356
Coefficient of variation (CV)0.25010561
Kurtosis3.6281119
Mean10.770393
Median Absolute Deviation (MAD)1
Skewness-1.6069377
Sum3565
Variance7.2562117
MonotonicityNot monotonic
2024-09-15T14:30:57.041963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
12 72
21.8%
11 65
19.6%
13 52
15.7%
10 35
10.6%
9 26
 
7.9%
14 24
 
7.3%
8 18
 
5.4%
7 12
 
3.6%
6 8
 
2.4%
15 6
 
1.8%
Other values (6) 13
 
3.9%
ValueCountFrequency (%)
0 4
 
1.2%
1 3
 
0.9%
2 1
 
0.3%
3 2
 
0.6%
4 2
 
0.6%
5 1
 
0.3%
6 8
 
2.4%
7 12
3.6%
8 18
5.4%
9 26
7.9%
ValueCountFrequency (%)
15 6
 
1.8%
14 24
 
7.3%
13 52
15.7%
12 72
21.8%
11 65
19.6%
10 35
10.6%
9 26
 
7.9%
8 18
 
5.4%
7 12
 
3.6%
6 8
 
2.4%

exam_questions_attempted
Real number (ℝ)

Distinct12
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.65861
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-09-15T14:30:57.086865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q116
median16
Q316
95-th percentile16
Maximum32
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2646312
Coefficient of variation (CV)0.1446253
Kurtosis28.552395
Mean15.65861
Median Absolute Deviation (MAD)0
Skewness-3.132256
Sum5183
Variance5.1285544
MonotonicityNot monotonic
2024-09-15T14:30:57.130999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
16 317
95.8%
5 3
 
0.9%
4 2
 
0.6%
6 1
 
0.3%
32 1
 
0.3%
10 1
 
0.3%
15 1
 
0.3%
1 1
 
0.3%
2 1
 
0.3%
3 1
 
0.3%
Other values (2) 2
 
0.6%
ValueCountFrequency (%)
1 1
 
0.3%
2 1
 
0.3%
3 1
 
0.3%
4 2
0.6%
5 3
0.9%
6 1
 
0.3%
7 1
 
0.3%
10 1
 
0.3%
12 1
 
0.3%
15 1
 
0.3%
ValueCountFrequency (%)
32 1
 
0.3%
16 317
95.8%
15 1
 
0.3%
12 1
 
0.3%
10 1
 
0.3%
7 1
 
0.3%
6 1
 
0.3%
5 3
 
0.9%
4 2
 
0.6%
3 1
 
0.3%

unique_practice_questions_answered
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.072508
Minimum2
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-09-15T14:30:57.185859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q186.5
median96
Q398
95-th percentile98
Maximum98
Range96
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation26.781397
Coefficient of variation (CV)0.3223858
Kurtosis1.5586393
Mean83.072508
Median Absolute Deviation (MAD)2
Skewness-1.7304919
Sum27497
Variance717.24321
MonotonicityNot monotonic
2024-09-15T14:30:57.247089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98 136
41.1%
96 91
27.5%
95 10
 
3.0%
6 4
 
1.2%
94 3
 
0.9%
15 3
 
0.9%
61 3
 
0.9%
25 2
 
0.6%
43 2
 
0.6%
50 2
 
0.6%
Other values (55) 75
22.7%
ValueCountFrequency (%)
2 1
 
0.3%
4 1
 
0.3%
5 1
 
0.3%
6 4
1.2%
7 1
 
0.3%
9 1
 
0.3%
10 1
 
0.3%
11 1
 
0.3%
12 1
 
0.3%
15 3
0.9%
ValueCountFrequency (%)
98 136
41.1%
97 2
 
0.6%
96 91
27.5%
95 10
 
3.0%
94 3
 
0.9%
90 2
 
0.6%
89 1
 
0.3%
88 2
 
0.6%
87 1
 
0.3%
86 1
 
0.3%

unique_practice_questions_answered_scaled_by_max
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84767865
Minimum0.020408163
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-09-15T14:30:57.304574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.020408163
5-th percentile0.19387755
Q10.88265306
median0.97959184
Q31
95-th percentile1
Maximum1
Range0.97959184
Interquartile range (IQR)0.11734694

Descriptive statistics

Standard deviation0.27327956
Coefficient of variation (CV)0.3223858
Kurtosis1.5586393
Mean0.84767865
Median Absolute Deviation (MAD)0.020408163
Skewness-1.7304919
Sum280.58163
Variance0.074681717
MonotonicityNot monotonic
2024-09-15T14:30:57.364852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 136
41.1%
0.9795918367 91
27.5%
0.9693877551 10
 
3.0%
0.0612244898 4
 
1.2%
0.9591836735 3
 
0.9%
0.1530612245 3
 
0.9%
0.6224489796 3
 
0.9%
0.2551020408 2
 
0.6%
0.4387755102 2
 
0.6%
0.5102040816 2
 
0.6%
Other values (55) 75
22.7%
ValueCountFrequency (%)
0.02040816327 1
 
0.3%
0.04081632653 1
 
0.3%
0.05102040816 1
 
0.3%
0.0612244898 4
1.2%
0.07142857143 1
 
0.3%
0.09183673469 1
 
0.3%
0.1020408163 1
 
0.3%
0.112244898 1
 
0.3%
0.1224489796 1
 
0.3%
0.1530612245 3
0.9%
ValueCountFrequency (%)
1 136
41.1%
0.9897959184 2
 
0.6%
0.9795918367 91
27.5%
0.9693877551 10
 
3.0%
0.9591836735 3
 
0.9%
0.9183673469 2
 
0.6%
0.9081632653 1
 
0.3%
0.8979591837 2
 
0.6%
0.887755102 1
 
0.3%
0.8775510204 1
 
0.3%

Interactions

2024-09-15T14:30:55.553958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:47.093231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:50.476272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:51.297293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:52.150500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:52.942022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:53.837976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:54.738131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:56.158626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:47.858657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:51.008365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:51.874766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:52.665022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:53.545918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:54.368369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:55.274822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:56.204610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:48.295967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:51.053390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:51.917556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:52.708245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:53.591468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:54.412442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:55.317014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:56.246019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:48.604406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:51.093600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:51.955812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:52.745289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:53.631584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:54.453736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:55.353485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:56.287619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:49.008441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:51.135252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:51.997823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:52.782842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:53.670992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:54.493225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:55.391543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:56.331492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:49.338108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:51.175095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:52.036914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:52.824023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:53.712552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:54.612102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:55.430820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:56.371220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:49.764621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:51.215652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:52.076163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:52.863169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:53.753824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:54.650892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:55.471599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:56.410932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:50.095263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:51.255146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:52.111876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:52.902655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:53.795809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:54.699327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T14:30:55.512098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-09-15T14:30:57.404203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
exam_pointsexam_questions_attemptedprior_insutruction_reporting_frequencyprior_insutruction_reporting_frequency_scaled_by_maxproportion_of_prior_insutrctionunique_practice_questions_answeredunique_practice_questions_answered_scaled_by_maxuser_id
exam_points1.0000.3110.3270.3270.0970.1970.197-0.014
exam_questions_attempted0.3111.0000.2990.299-0.0050.2510.2510.003
prior_insutruction_reporting_frequency0.3270.2991.0001.0000.0230.8410.841-0.020
prior_insutruction_reporting_frequency_scaled_by_max0.3270.2991.0001.0000.0230.8410.841-0.020
proportion_of_prior_insutrction0.097-0.0050.0230.0231.0000.0580.0580.183
unique_practice_questions_answered0.1970.2510.8410.8410.0581.0001.0000.047
unique_practice_questions_answered_scaled_by_max0.1970.2510.8410.8410.0581.0001.0000.047
user_id-0.0140.003-0.020-0.0200.1830.0470.0471.000

Missing values

2024-09-15T14:30:56.462324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-15T14:30:56.527917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

user_idprior_insutruction_reporting_frequencyproportion_of_prior_insutrctionprior_insutruction_reporting_frequency_scaled_by_maxexam_pointsexam_questions_attemptedunique_practice_questions_answeredunique_practice_questions_answered_scaled_by_max
042930160.31251.01316981.000000
161610161.00001.0916981.000000
261835160.06251.01216981.000000
349694160.43751.01216960.979592
461693160.62501.01216960.979592
5257161.00001.01416981.000000
625581160.25001.0816981.000000
761659160.06251.01016981.000000
861650160.50001.01216960.979592
950259160.00001.01316981.000000
user_idprior_insutruction_reporting_frequencyproportion_of_prior_insutrctionprior_insutruction_reporting_frequency_scaled_by_maxexam_pointsexam_questions_attemptedunique_practice_questions_answeredunique_practice_questions_answered_scaled_by_max
321341030NaN0.061660.061224
322402060NaN0.0816360.367347
323396850NaN0.01216150.153061
324124760NaN0.01360.061224
325272130NaN0.04790.091837
326278760NaN0.081670.071429
327266560NaN0.01112110.112245
328483100NaN0.01116370.377551
329484180NaN0.0816250.255102
330539460NaN0.0131660.061224